Insecure Design in Flask with Dynamodb

An insecure design occurs when application architecture and data-flow choices expose sensitive operations or allow unsafe access patterns. In a Flask application using Amazon DynamoDB, this commonly arises from designing endpoints that directly expose DynamoDB keys, construct queries from unchecked user input, or fail to enforce authorization at the data-access layer. Because DynamoDB is a managed NoSQL store, developers may assume the service itself enforces safety, but the responsibility for query construction and access control lies with the application.

Consider a design where an endpoint accepts a path parameter user_id and uses it as the Key in a GetItem or Query against a DynamoDB table without validating format or scope. If the table uses a composite primary key (partition key + sort key), a flawed design might concatenate user input to form the sort key without normalization or strict allow-listing, enabling BOLA/IDOR across related items. For example, an attacker could iterate over known IDs to access other users’ records when the application fails to scope queries to the authenticated subject.

Insecure design also appears when the application embeds sensitive metadata in item attributes (e.g., role flags or administrative states) and relies solely on client-side filtering. DynamoDB’s response contains the full item; if the API returns the item as-is, a client can infer privilege levels or internal logic. This is an insecure design choice because visibility into attributes should be enforced server-side through authorization checks rather than assumed through obscurity.

Another pattern is using high-level abstractions that hide conditional permissions. If a Flask route builds a KeyConditionExpression or FilterExpression from raw strings or unsanitized JSON, the design may unintentionally expose more data than intended. For instance, a developer might write KeyConditionExpression='user_id = :uid AND status = :status' with bound variables but then allow status to be client-supplied, permitting enumeration of items across statuses and bypassing intended access boundaries.

These issues map to the OWASP API Top 10 category Broken Object Level Authorization (BOLA) and are detectable by middleBrick’s BOLA/IDOR checks. middleBrick scans the unauthenticated attack surface and, when an OpenAPI spec is provided, cross-references spec definitions with runtime behavior to highlight endpoints where identifiers are exposed or insufficiently scoped. Because middleBrick runs 12 security checks in parallel, it can surface insecure design patterns related to authorization, input validation, and data exposure in a single scan that typically completes within 5–15 seconds.

To illustrate, a vulnerable Flask route might look like this, demonstrating an insecure design where user input directly shapes DynamoDB keys without authorization scoping:

import boto3
from flask import Flask, request

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('Users')

@app.route('/users/<user_id>')
def get_user(user_id):
    # Insecure design: no validation, no authorization scope
    response = table.get_item(Key={'user_id': user_id})
    return response.get('Item', {})

In this example, the design does not verify that the requesting subject owns the requested user_id, nor does it validate the format of user_id. An attacker can enumerate IDs horizontally or vertically if key patterns are predictable. middleBrick’s LLM/AI Security checks do not apply here because this is not an LLM endpoint, but the scan’s Authentication and BOLA checks will flag the missing ownership verification.

Remediation centers on enforcing strict input validation, scoping queries to the authenticated subject, and avoiding exposure of internal keys. Design endpoints so that the partition key is derived from the authenticated identity, and use sort keys to enforce tenant or ownership boundaries. Never allow client-controlled values to directly dictate DynamoDB key attributes without normalization and allow-listing.

Use the AWS SDK for Python (Boto3) with resource-based abstractions for clarity and safety. Always parameterize expressions and avoid string concatenation for condition building. Below is a secure redesign of the earlier route, where the API authenticates the caller (e.g., via session or token) and the query is scoped to the authenticated user’s partition key.

import boto3
from flask import Flask, request, g
from functools import wraps

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('Users')

def require_auth(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        # Example: derive identity from a token or session
        user_identity = getattr(g, 'user', None)
        if not user_identity:
            return {'error': 'unauthorized'}, 401
        return f(*args, **kwargs)
    return decorated

@app.route('/users/me')
@require_auth
def get_current_user():
    # Secure design: scope to authenticated partition key
    response = table.get_item(
        Key={
            'user_id': g.user['sub']  # subject claim from auth token
        }
    )
    return response.get('Item', {})

This design ensures that the partition key is derived from an authenticated identity context (g.user['sub']) rather than from an untrusted route parameter. It mitigates horizontal IDOR because users can only fetch their own item by partition key.

For endpoints that require querying by non-key attributes, use a Global Secondary Index (GSI) whose partition key maps to the owning user. Then, include the user identifier in the key condition expression to enforce tenant isolation. The following example demonstrates a query on a GSI called user-status-index, where the partition key of the GSI is user_id and the sort key is status. The design prevents enumeration across users because the query requires the authenticated user ID.

import boto3
from flask import Flask, g

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('Users')

@app.route('/users/me/active')
def get_active_items():
    user_id = g.user['sub']
    response = table.query(
        IndexName='user-status-index',
        KeyConditionExpression='user_id = :uid AND status = :status',
        ExpressionAttributeValues={
            ':uid': user_id,
            ':status': 'ACTIVE'
        }
    )
    return response.get('Items', [])

Input validation should normalize identifiers and enforce length and pattern constraints. For identifiers that are not controlled by DynamoDB keys, use allow-listing where possible and avoid passing raw values into KeyConditionExpression or FilterExpression via string interpolation. Instead, use expression attribute values to bind parameters safely.

middleBrick’s CLI can be used to validate remediation by scanning the API endpoint after changes: middlebrick scan <url>. The CLI outputs JSON or text reports that highlight remaining BOLA risks, input validation issues, and data exposure findings. Teams can integrate middleBrick into CI/CD with the GitHub Action to fail builds if the security score drops below a chosen threshold, ensuring insecure designs are caught before deployment.

For continuous assurance, the Pro plan provides scheduled scans and alerts, while the MCP Server lets you trigger scans directly from AI coding assistants in your IDE. These integrations help maintain secure design practices as the API evolves.